In this paper, we consider a full.duplex multiple.input multiple.output(MIMO) relaying network with the decode.and.forward(DF) protocol. Due to the full.duplex transmissions, the self.interference from the relay trans...In this paper, we consider a full.duplex multiple.input multiple.output(MIMO) relaying network with the decode.and.forward(DF) protocol. Due to the full.duplex transmissions, the self.interference from the relay transmitter to the relay receiver degrades the system performance. We thus propose an iterative beamforming structure(IBS) to mitigate the self.interference. In this method, the receive beamforming at the relay is optimized to maximize the signal.to.interference.plus.noise.ratio(Max.SINR), while the transmit beamforming at the relay is optimized to maximize the signal.to.leakage.plusnoise.ratio(Max.SLNR). To further improve the performance, the receive and transmit beamforming matrices are optimized between Max.SINR and Max.SLNR in an iterative manner. Furthermore, in the presence of the residual self.interference, a low.complexity whitening.filter(WF) maximum likelihood(ML) detector is proposed. In this detector, a WF is designed to transform a colored interference.plus.noise to a white noise, while the singular value decomposition is used to convert coupled spatial subchannels to parallelindependent ones. From simulations, we find that the proposed IBS performs much better than the existing schemes. Also, the proposed low.complexity detector significantly reduces the complexity of the conventional ML(CML) detector from exponential time(an exponential function of the number of the source transmit antennas) to polynomial one while achieving a slightly better BER performance than the CML due to interference whitening.展开更多
An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVD...An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.展开更多
基金supported in part by the National Natural Science Foundation of China (Nos. 61271230, 61472190, and 61501238)the Open Research Fund of National Key Laboratory of Electromagnetic Environment, China Research Institute of Radiowave Propagation (No. 201500013)+4 种基金the open research fund of National Mobile Communications Research Laboratory, Southeast University, China (No. 2013D02)the Research Fund for the Doctoral Program of Higher Education of China (No. 20113219120019)the Foundation of Cloud Computing and Big Data for Agriculture and Forestry (117-612014063)the China Postdoctoral Science Foundation (2016M591852)Postdoctoral research funding program of Jiangsu Province (1601257C)
文摘In this paper, we consider a full.duplex multiple.input multiple.output(MIMO) relaying network with the decode.and.forward(DF) protocol. Due to the full.duplex transmissions, the self.interference from the relay transmitter to the relay receiver degrades the system performance. We thus propose an iterative beamforming structure(IBS) to mitigate the self.interference. In this method, the receive beamforming at the relay is optimized to maximize the signal.to.interference.plus.noise.ratio(Max.SINR), while the transmit beamforming at the relay is optimized to maximize the signal.to.leakage.plusnoise.ratio(Max.SLNR). To further improve the performance, the receive and transmit beamforming matrices are optimized between Max.SINR and Max.SLNR in an iterative manner. Furthermore, in the presence of the residual self.interference, a low.complexity whitening.filter(WF) maximum likelihood(ML) detector is proposed. In this detector, a WF is designed to transform a colored interference.plus.noise to a white noise, while the singular value decomposition is used to convert coupled spatial subchannels to parallelindependent ones. From simulations, we find that the proposed IBS performs much better than the existing schemes. Also, the proposed low.complexity detector significantly reduces the complexity of the conventional ML(CML) detector from exponential time(an exponential function of the number of the source transmit antennas) to polynomial one while achieving a slightly better BER performance than the CML due to interference whitening.
基金supported by the National Science&Technology Pillar Program(2013BAF07B03)Zhejiang Provincial Natural Science Foundation of China(LY13F010009)
文摘An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive （RJIO-DA） is proposed for large-array scenarios. Based on the framework of minimum variance distortionless response （MVDR）, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.
